We present and validate the image analysis algorithm μ-scope to capture personal mobility devices’ (PMDs) movement characteristics and extract their movement dynamics even when they interact with each other and with pedestrians. Experimental data were used for validation of the proposed algorithm. Data were collected through a large-scale, semicontrolled, real-track experiment at the University of Patras campus. Participants (N = 112) included pedestrians, cyclists, and e-scooter drivers. The experiment was video recorded, and μ-scope was used for trajectory extraction. Some of the participants had installed, beforehand, the Phyphox application in their smartphones. Phyphox accurately measures x-y-z acceleration rates and was used, in our case, as the baseline measurement (i.e., “ground truth”). Statistical comparison between Phyphox and camera-based measurements shows very low difference in most cases. High pedestrian densities were the only case where relatively high root mean square errors were registered. The proposed algorithm can be thus considered capable of producing reliable speed and acceleration estimates. Low-quality conventional smartphone cameras were used in this experiment. As a result, the proposed method can be easily applied to all urban contexts under normal traffic conditions, but eventually not in the case of special or emergency events generating very high pedestrian densities.

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The purpose of this research is to obtain a database of Peruvian warning traffic signs and propose a tool to automate the road inventory process using image processing algorithms. The database consists of 2026 images of Peruvian warning traffic signs, to detect and recognize them on Av. Eduardo Habich located in Metropolitan Lima, also proposed the following methodology tha t is divided into two parts: The first part consists of collecting da ta in the field for the creation of the database; and the second part consists of the processing of information in the cabinet, where the detection and recognition algorithm for information processing is proposed. The detection stage consists of the use of color and sha p e filters, as well as the performance of two-color models, HSV a nd normalized RGB, for the characteristic yellow color of warning signs. The recognition stage consists of the use of supervised classification tools with the algorithm called support vector machines. Finally, with the development of this research, it was possible to obtain an algorithm that allows the detection of tra ffic signs with a recognition percentage of 62.5% and a solid database that can be fed back and give rise to future research in the automation of traffic signals. road inventories.